Although men have long held the lead in motor vehicle crashes (MVCs), recent international research indicates that women, particularly young female drivers, are closing the gap. The relative increase in females involved in crashes has been associated with an increase in crash exposure, together with other mediating factors such as problems in vehicle handling, mastering traffic situations, and changes in attitudes toward risk. The goal of this proposed study is to perform a secondary analysis of large, national datasets to identify the age/race/ethnic groups of females increasingly vulnerable to the risk of being involved in MVCs (under different driving conditions and behaviors). Based on a limited version of the Hierarchical Levels of Driving Behavior's model, this proposed study will take advantage of the rich crash-related information provided by the 1990-2004 Fatality Analysis Reporting System (FARS), Crashworthiness Data System (CDS), and General Estimates System (GES) to identify the groups of females at risk. After including relevant geographical, US Census-based information to these files via zip code identifiers, a General Latent Variable Model (GLVM) will be applied. Finally, with the information at hand, we will apply a decomposition method to estimate the probability of crash injuries at each of the three hierarchical levels in our conceptual model: "vehicle maneuvering" (i.e., inadequate basic driving skills), "mastering traffic situations" (e.g., failure to obey a traffic law); and "goals and context of driving" (e.g., drink and drive). This understanding will be a crucial contribution to the design of future research aimed at realizing the reasons why some age and race/ethnic groups and subgroups of female drivers are at an increasing risk of MVC injuries. Public Health Relevance Statement -- This study will evaluate the risk of motor vehicle crash (MVC) for women of different age and race/ethnic groups. By identifying those groups of women at an increasing risk, this study will contribute to the design of efficient prevention policies tailored specifically to this most at-risk group. [unreadable] [unreadable] [unreadable]